Paper Citation Record · LEDGER

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models

As of 19 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2506.18732.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.18732 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:19:35.569500Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

30 of 30 outbound references displayed

verified exact1
verified fuzzy24
unresolved5
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6c37bcb1-a62c-4121-897d-b7e9390cb285 · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data,.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models Communication-efficient learning of deep networks from decentralized data,

Reference 1

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Source-reported events for the cited work

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Observation cd993d67-7b4c-4e8c-b07f-9d06f36483f1 · outbound

This paper cites Advances and open problems in federated learning,.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models Advances and open problems in federated learning,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:40.679553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7ba5b5c1-aeaf-44e2-9dba-5041dc791360 · outbound

This paper cites Fedmbp: Multi-branch prototype federated learning on heterogeneous data,.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models Fedmbp: Multi-branch prototype federated learning on heterogeneous data,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:40.438881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 81268047-90e1-4cc6-8946-fd86f29b19ab · outbound

This paper cites Dense contrastive-based federated learning for dense prediction tasks on medical images,.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models Dense contrastive-based federated learning for dense prediction tasks on medical images,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:40.281981Z

Source-reported events for the cited work

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Observation 2a8f4184-f54e-4a9a-b4ad-57fc79510c64 · outbound

This paper cites Rethinking architecture design for tackling data heterogeneity in federated learning,.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models Rethinking architecture design for tackling data heterogeneity in federated learning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:40.060283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4bf61ecb-afe2-47cd-982b-b8a4f3375164 · outbound

This paper cites Buffalo: Biomedical vision-language understanding with cross-modal prototype and federated foundation model collaboration,.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models Buffalo: Biomedical vision-language understanding with cross-modal prototype and federated foundation model collaboration,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:39.778341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c2239d6d-7772-43fa-b35d-0921984cf48b · outbound

This paper cites Advances and Open Challenges in Federated Foundation Models.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models Advances and Open Challenges in Federated Foundation Models

Reference 7

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Source-reported events for the cited work

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Observation 7c6441d9-e017-4bbb-8774-3b335b286fff · outbound

This paper cites The prospect of enhancing large-scale heterogeneous federated learning with foundation models,.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models The prospect of enhancing large-scale heterogeneous federated learning with foundation models,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:39.599658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3d2581ff-aad3-4596-a232-103d7b381a68 · outbound

This paper cites Trustworthy Federated Learning: A Survey.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models Trustworthy Federated Learning: A Survey

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:19:35.876870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 32c55d32-c8f3-4e9d-8d42-a6be3dade389 · outbound

This paper cites Towards fairness-aware feder- ated learning,.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models Towards fairness-aware feder- ated learning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:39.492359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 55b26151-1d9c-4d6c-a385-1d3f140d56e3 · outbound

This paper cites Proportionally fair hospital collaborations in federated learning of histopathology images,.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models Proportionally fair hospital collaborations in federated learning of histopathology images,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:39.371067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ed2f35a4-b493-444a-bd54-167ff87b2439 · outbound

This paper cites Unified fair federated learning for digital healthcare,.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models Unified fair federated learning for digital healthcare,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:39.188891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e8e6635d-061a-4ba7-9938-e18207c01791 · outbound

This paper cites Al- gorithmic fairness in artificial intelligence for medicine and healthcare,.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models Al- gorithmic fairness in artificial intelligence for medicine and healthcare,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:38.976174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 87352d83-a65d-4092-8931-c2c1d8e14ebd · outbound

This paper cites Fair federated learning for heterogeneous data,.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models Fair federated learning for heterogeneous data,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:38.855310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 42c02484-3871-4472-804c-671d7e91a6a1 · outbound

This paper cites Fair federated learning with biased vision-language models,.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models Fair federated learning with biased vision-language models,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:38.711394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 01ebca58-37d5-42dd-89a2-094c8a66329c · outbound

This paper cites Fairness- aware agnostic federated learning,.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models Fairness- aware agnostic federated learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:38.534289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c82d5194-b645-45b2-bc7e-50d6179699da · outbound

This paper cites Fairfed: Enabling group fairness in federated learning,.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models Fairfed: Enabling group fairness in federated learning,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:38.351680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8652098d-3b70-4578-8c4a-1ad8f2edd44d · outbound

This paper cites Fairfl: A fair federated learning approach to reducing demographic bias in privacy-sensitive classification models,.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models Fairfl: A fair federated learning approach to reducing demographic bias in privacy-sensitive classification models,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:38.127826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 66a262e5-3013-4b53-8bb2-dec380bb548b · outbound

This paper cites Fair-fate: Fair federated learning with momentum,.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models Fair-fate: Fair federated learning with momentum,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:37.888007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 46c33e16-30a7-4fd8-924b-814c1b44f7c9 · outbound

This paper cites GLOCALFAIR: Jointly Improving Global and Local Group Fairness in Federated Learning.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models GLOCALFAIR: Jointly Improving Global and Local Group Fairness in Federated Learning

Reference 20

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Source-reported events for the cited work

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Observation 365f7f43-cfb8-4c92-ac7f-464f7ffc45b7 · outbound

This paper cites Bias mitigation in federated learning for edge computing,.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models Bias mitigation in federated learning for edge computing,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:37.578272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2a6e9b26-e021-4236-ba66-ddaa5172041b · outbound

This paper cites Fedcsl: A scalable and accurate approach to federated causal structure learning,.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models Fedcsl: A scalable and accurate approach to federated causal structure learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:37.377471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation dfcc6161-7ec4-4394-9866-4e57a85491ab · outbound

This paper cites Causal representation learning via counterfactual intervention,.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models Causal representation learning via counterfactual intervention,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:37.179640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f2d94276-d50c-42b7-ae6e-c4e64d4208b9 · outbound

This paper cites When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

Reference 24

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Source-reported events for the cited work

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Observation caccc318-fbd6-4de5-aeb5-2673b630450a · outbound

This paper cites A Survey on Group Fairness in Federated Learning: Challenges, Taxonomy of Solutions and Directions for Future Research.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models A Survey on Group Fairness in Federated Learning: Challenges, Taxonomy of Solutions and Directions for Future Research

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T23:19:34.982565Z

Source-reported events for the cited work

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Observation 49f86f98-f3c7-439d-9ad8-5955fb9a43e1 · outbound

This paper cites Feature selection under fairness and performance constraints,.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models Feature selection under fairness and performance constraints,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:36.958331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fc70e326-c4c2-415e-82b2-fa5af9fcd23e · outbound

This paper cites Improving fairness in ai models on electronic health records: The case for federated learning methods,.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models Improving fairness in ai models on electronic health records: The case for federated learning methods,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:36.748158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1c6747c7-81cd-4f66-87fb-f18a88ad151a · outbound

This paper cites Analyzing the impact of personalization on fairness in federated learning for healthcare,.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models Analyzing the impact of personalization on fairness in federated learning for healthcare,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:36.530743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d2fd9607-64a7-4164-a88a-b3652d430aa0 · outbound

This paper cites Causal machine learning for predicting treatment outcomes,.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models Causal machine learning for predicting treatment outcomes,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:36.293856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 55098d15-01a9-4cb7-aec5-5dbcd0d14636 · outbound

This paper cites Causal discovery for fairness,.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models Causal discovery for fairness,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:19:36.064731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.